Currently financial institution customer loan resolution modeling is conducted at the aggregate level. Aggregate level modeling assumes that a certain segment of the overall loan population behaves a certain way and based on those behaviors predicts how that segment will behave for the area of interest. For loan resolution modeling, aggregate level processing, determines how a specific segment of the overall financial customer population is likely to resolve the loan within a predefined period of time.
In loan resolution, many events may occur within the life of the loan, for example, events related to the current/late state of the loan, such as staying current on loan payment, becoming late on loan payment and events related to servicing of the loan, such as modification/short sale of the loan, a bankruptcy by the customer, foreclosure/Real Estate Owned (REO), becoming the potential workout population and the like. Each of these events tend to occur at random times throughout the life of the loan and may depend on other factors, such as availability of financial resources, market demand, and customer attributes, which are also stochastic by nature.
Therefore, a need exists to develop systems, methods, computer program products and the like which model loan resolution at the individual customer and/or account level as opposed to at the aggregate level. Such loan resolution modeling needs to be able to capture the stochastic nature of the events that occur within the loan lifecycle. The desired solution should take into account not only the randomness of the events but also the complexity, interdependency and importance of timing.